Prior distributions for autologistic regression models

Dipankar Bandyopadhyay writes:

I am currently running an autologistic regression model where I have some fixed effects and also spatial (autlogistic) terms. Is there any recommendation from you on the appropriate choice of prior on the variance when I put a normal prior on the regression coefficients? I mean, do you recommend a folded-t, or a half-cauchy, or a uniform over the traditional inverse gamma, and in such a case, where can I get the WinBUGS codes to put folded-t, or half cauchy/half-normal priors?

My reply: I don’t have any direct experience with this, but I think I’d want to go with a weakly informative prior such as a half-Cauchy as in my 2006 paper, or else a hierarchical model if you have several difference variance parameters. I think this could be done using similar Bugs code as in the appendix to that paper.